Wednesday, March 27, 2013

It has become much easier to assess the academic impact of published papers and authors (Google Citations Page* and T. Reuters). However, the assessment of academic impact on public policy is not that straightforward, although it's a crucial issue for Research Institutes, Think-thanks and Funding Agencies.

A team at LSE leads a project on the subject and they are developing quantitative metrics for measuring the impact of research in the 'public sphere'. It's the Impact of Social Sciences Project. self-recommending Some podcasts of their latest events should be available here:

*I encourage you to register at Google Scholar Citations. This is one of the best ways for academics to compute citation metrics and track them over time. You only need a gmail account and Google does the rest for you. Besides, anyone can register!

In a recent Freakonomics podcast, Donald Shoup (UCLA) talked about his studies on the costs of free parking. Shoup's main argument is that most parking spaces are underpriced, especially in great urban areas. Ok, it's hard to disagree on that. But still, I would like to see some more evidence that reducing parking actually reduces traffic congestion.

Monday, March 18, 2013

The Economist Magazine has published a short piece where they highlight Wolfgang Lutz's arguments on the importance of educational attainment to the potential benefits countries may get from their demographic dividend.

Now imagine if you could have acess to real time data on traffic conditions on arterial roads in several cities around the globe. There is one company that generates these data. I know what you're thinking: "Damn these guys from Google are awesome!"

Acutally this is not a new project. Google has started it aroud 2009 and now it covers. several cities around the globe. They basically track anonymous locations from smartphones to gauge traffic conditions in real time:

The Google Maps Traffic Layer (web version) also inclues traffic prediction for any day of the week and time of the day, based on past conditions. Neat! Now it should be very interesting to compare/validate Google's traffic data to "real data" from more traditional sources.

Wednesday, March 6, 2013

A friend of mine pointed me out to this study by Michael I. Norton (Harvard) and Dan Ariely (Duke) where they contrast the current distribution of wealth in the USA to the ideal level of wealth inequality expected by americans.

In August 2011, several areas of London experienced episodes of large-scale disorder, comprising looting, rioting and violence. Much subsequent discourse has questioned the adequacy of the police response, in terms of the resources available and strategies used. In this article, we present a mathematical model of the spatial development of the disorder, which can be used to examine the effect of varying policing arrangements. The model is capable of simulating the general emergent patterns of the events and focusses on three fundamental aspects: the apparently-contagious nature of participation; the distances travelled to riot locations; and the deterrent effect of policing. We demonstrate that the spatial configuration of London places some areas at naturally higher risk than others, highlighting the importance of spatial considerations when planning for such events. We also investigate the consequences of varying police numbers and reaction time, which has the potential to guide policy in this area.

The paper is published in Portuguese only the title translation is my bad but you may read the abstract:

We estimate the contribution of
the wage differential between workers with the same attributes in the public
and private sectors to the household per capita income inequality in Brazil.
The estimate is based on counterfactual simulations and the contribution to
inequality on a factor decomposition of the Gini coefficient. Data comes from
the Brazilian National Household Survey PNAD 2009. The differential corresponds
approximately to 17% of the wage bill of workers in the public sector, is
regressive and highly concentrated. However, because it amounts to a small
share of the total income (1%) its contribution to the total inequality is of
3%. The sector composition effects on inequality are times higher than the
segmentation (price) effects. These conclusions are robust to changes in the
definition of the sectors and to different estimation techniques.